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Bayesian inference analyses of the polygenic architecture of rheumatoid arthritis

Abstract

The genetic architectures of common, complex diseases are largely uncharacterized. We modeled the genetic architecture underlying genome-wide association study (GWAS) data for rheumatoid arthritis and developed a new method using polygenic risk-score analyses to infer the total liability-scale variance explained by associated GWAS SNPs. Using this method, we estimated that, together, thousands of SNPs from rheumatoid arthritis GWAS explain an additional 20% of disease risk (excluding known associated loci). We further tested this method on datasets for three additional diseases and obtained comparable estimates for celiac disease (43% excluding the major histocompatibility complex), myocardial infarction and coronary artery disease (48%) and type 2 diabetes (49%). Our results are consistent with simulated genetic models in which hundreds of associated loci harbor common causal variants and a smaller number of loci harbor multiple rare causal variants. These analyses suggest that GWAS will continue to be highly productive for the discovery of additional susceptibility loci for common diseases.

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Figure 1: Association of polygenic risk scores with common disease case-control status in independent validation datasets.
Figure 2: Posterior probability densities of the number of associated SNPs and the total liability-scale variance explained for the Bayesian analysis of the polygenic analysis results.
Figure 3: Posterior probability distributions of the relative risk and minor allele frequency of the inferred disease-associated SNPs.
Figure 4: Causal variants underlying the rheumatoid arthritis polygenic disease architecture inferred from the GWAS data.

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Acknowledgements

R.M.P. is supported by grants from the US National Institutes of Health (NIH) (R01-AR057108, R01-AR056768, U01-GM092691 and R01-AR059648) and holds a Career Award for Medical Scientists from the Burroughs Wellcome Fund. S.R. is supported by an NIH Career Development Award (K08AR055688-01A1). The Brigham Rheumatoid Arthritis Sequential Study Registry is supported by a grant from Crescendo and Biogen-Idec. The North American Rheumatoid Arthritis Consortium is supported by the NIH (NO1-AR-2-2263 and RO1-AR44422). This research was also supported in part by the Intramural Research Program of the National Institute of Arthritis, Musculoskeletal and Skin Diseases of the NIH and by a Canada Research Chair and grants to K.A.S. from the Canadian Institutes for Health Research (MOP79321 and IIN-84042) and the Ontario Research Fund (RE01061). We acknowledge S. Purcell, A. Price and N. Zaitlen for help with the design and implementation of the study and analysis.

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Study design: R.M.P., E.A.S., S.R. and P.I.W.d.B. Analysis: E.A.S. (lead), D.W., G.T., J.G.-A., R.D., B.F.V. (primary contributors), R.C., H.J.K. and F.A.S.K. Samples and data: C.W., S.K., B.F.V., the Myocardial Infarction Genetics Consortium, the Diabetes Genetics Replication and Meta-analysis Consortium, J.W., L.A., P.K.G., K.A.S. and R.M.P. Writing: R.M.P., E.A.S. (leads), D.W., P.K. (primary contributors) and all other authors.

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Correspondence to Eli A Stahl or Robert M Plenge.

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A full list of members is provided in the Supplementary Note.

A full list of members is provided in the Supplementary Note.

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Stahl, E., Wegmann, D., Trynka, G. et al. Bayesian inference analyses of the polygenic architecture of rheumatoid arthritis. Nat Genet 44, 483–489 (2012). https://doi.org/10.1038/ng.2232

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